Affordable & Responsible AI |

MRIQT — Image-Quality Transfer for Ultra-Low-Field MRI

Fri, 30 Oct 2026 00:00:00 +0000

Slide to compare the portable ultra-low-field scan (uLF, right) with the MRIQT enhancement (left). Use the buttons to switch between two patients.

MRIQT uLF (0.064 T)

Neonatal brain MRI from the University Hospital Bonn cohort. Ethical approval: Ethics Committee of the University of Bonn (Nr. 167/22).

· · · Accepted at , London

Why it matters

Infants change fast in the first weeks of life: their brains differ from adult brains in both anatomy and tissue contrast. In neonatal intensive care units, many infants have neurological conditions that need repeated MRI, yet standard high-field MRI means disconnecting them from life support and moving them to a scanner room — slow, often requiring sedation and exposure to loud noise.

Portable ultra-low-field MRI (0.064 T) brings the scanner to the bedside, but with low signal-to-noise and reduced diagnostic quality. MRIQT closes that gap: it performs 3D image-quality transfer (uLF → high-field) with a conditional diffusion model guided by physics-aware k-space simulation and an anatomy-preserving 3D perceptual objective. The cohort comprises neonates with an average age of two months and nine clinically relevant neurological pathologies, often occurring together.

How it works

Physics-aware degradation: realistic ultra-low-field scans are simulated from high-field ones in k-space.
3D conditional diffusion: the model enhances the scan while preserving anatomy.
Guidance and an SNR-weighted 3D perceptual loss reduce artefacts and hallucinations.

Training — MRIQT learns from physics-based uLF simulations to reconstruct anatomically faithful images from noisy inputs:

Inference — given a real uLF scan, MRIQT iteratively denoises a noisy initialisation while conditioning on the uLF measurement:

Results

Compared with competing methods on PSNR, LPIPS, MAE, MS-SSIM, SSIM and Pearson correlation:

Medical Pitch Contest Bonn

Wed, 14 Oct 2026 00:00:00 +0000

attended MIB Future Panel 2026 where he also pitched , along with , at the Medical Pitch Contest and our plan to commercialze the project. The MIB Future Panel is UKBonn’s annual networking event hosted by the Medical Imaging Center and UKBonn’s Transfer Team in Bonn. The goal of this event is to merge medical imaging and artificial intelligence (AI) in clinical practice and research with a panel discussion with specilists. The bulk of the event features a medical pitch contest featuring teams from the medical faculty at the University Hospital Bonn, where venture capital investors serve as a jury, evaluating the competing teams projects for a competing prize of €60,000. The event offers an opportunity for teams to network with investors and accelerator programs to move from the research phase into commercialization. Our work, MRIQT-Neo is a software that works on image quality transfer for MRI images, from the portable ultra-low-field MRI into high-quality, 3T- like images. The networking and feedback event provided a valuable feedback for us on how to

Entwicklung einer künstlichen Intelligenz zur ultraschallgestützten Detektion der Riesenzellarteriitis

Wed, 09 Sep 2026 00:00:00 +0000

MISTAI: Medical Intelligence & Skills Training in AI

Wed, 01 Jul 2026 00:00:00 +0000

The war in Ukraine has severely disrupted medical research and teaching. Digital pathology and AI offer a way to keep both going without depending on physical infrastructure. MISTAI is a bilateral cooperation between the University Hospital Bonn and Dnipro State Medical University (DSMU), funded by the Ministry of Culture and Science of North Rhine-Westphalia (MKW NRW).

Goals

Shared data infrastructure: digitise and validate at least 100 H&E and IHC whole-slide images from a DSMU cohort of muscle-invasive bladder cancer (MIBC), on a joint secure platform.
AI diagnostic pipeline: fine-tune state-of-the-art pathology foundation models on the combined Bonn–Dnipro data, including spatial analysis of tumour-infiltrating lymphocytes from routine H&E slides, inspired by the UC-TIL approach.
Clinical tool: a QuPath plugin that lets pathologists run the trained model directly in their existing workflow.
Capacity building: weekly remote collaboration, a one-week exchange visit to Bonn, and a Computational Pathology teaching module for the DSMU curriculum.
Follow-on work: a joint DFG/BMBF proposal outline and a MICCAI challenge concept.

Partners

Bonn contributes high-resolution slide scanners, AI infrastructure and expertise in computational medical imaging (Prof. Shadi Albarqouni) and translational lung and tumour pathology (Prof. Verena Tischler). DSMU contributes a clinically distinct bladder-cancer cohort and long-standing pathology expertise (Assoc. Prof. Nina Bondarenko, Department of Pathological Anatomy).

BONFOR Symposium & Promotionsbörse in Bonn

Tue, 16 Jun 2026 00:00:00 +0000

David represented our group at the BONFOR Symposium 2026 in Bonn, taking part in the Promotionsbörse (doctoral thesis fair) organized by the Medical Student Council alongside the symposium at the BMZ I. We have presented the work of the Computational Medical Imaging Research group (Albarqouni AG) at the Clinic for Diagnostic and Interventional Radiology and shared what doing a doctoral thesis with us looks like. It is a great opportunity to connect with the medical students looking for a thesis topic, walk them through our research directions in medical imaging and machine learning, and address their questions about getting started in research.

ArtFusion — Official codebase for the ArteryFusion model

Tue, 19 May 2026 00:00:00 +0000

Using the code

The repository README covers 1. training. See for step-by-step instructions.

AiM-BIGS

Mon, 23 Mar 2026 00:00:00 +0000

Using the code

The repository README covers the practical training for aim bigs course module — recycled from the bild summer school ( , datasets. See for step-by-step instructions.

Artificial intelligence for ultrasound-based detection of giant cell arteritis

Thu, 01 Jan 2026 00:00:00 +0000

Symposium on AI in Medicine & Food Science

Mon, 01 Dec 2025 00:00:00 +0000

Time: All times correspond to local Bonn time (CET)

Day 1 — Tuesday, 9 December 2025

Arrival & Check-in
📍 Youth Hostel Bonn, Haager Weg 42, 53127 Bonn

Day 2 — Wednesday, 10 December 2025

Symposium on AI in Medicine
📍 Baunscheidtstraße 17, 53113 Bonn

Time	Session	Speaker
09:30–10:00	Registration	—
10:00–10:30	Welcome Speech
10:30–11:45	Keynote Speech: Seeing, Sensing, Securing: AI in Modern Medicine
11:45–12:00	Therapeutic & Surgical Simulation in Medicine
12:00–12:15	AI-Driven Segmentation for Early Eye Disease Detection
12:15–12:30	“Ocean modeling in the Eastern Mediterranean: insights from physical based modelling, data assimilation and machine learning.
12:30–14:00	Lunch Break	—
14:00–14:15	FIDAL: Diversity-Aware Federated Active Learning Under Real-World Distribution Shifts
14:15–14:30	MRIQT: Physics-Aware Diffusion Model for Image Quality Transfer in Neonatal Ultra-Low-Field MRI
14:30–14:45	Rapid and affordable detection of Giant Cell Arteritis using B-mode ultrasound
14:45–15:15	Break	—
15:15–15:30	Teleradiology System Integrating DICOMweb and AI	Molka Yermani
15:30–15:45	Lightweight Bayesian Deep Learning for Point-of-Care Breast Ultrasound	Zeravan Ali
15:45–16:00	Modality‑Aware Deep Learning for Human Bone Fracture Recognition on HBFMID	Omar Al-Zakholi
16:00–16:15	Embedding BI-RADS Foundational Concepts for Cross-Domain and Cross-Modality Generalization in Breast Imaging
16:15–16:30	ML for Histopathological Classification of Conjunctival Melanocytic Intraepithelial Lesion
16:30–16:45	Affordable AI for Retinal Disease Detection
16:45–17:00	Wrap-up Discussion

Day 3 — Thursday, 11 December 2025

Symposium on Food Science
📍 Käthe-Kümmel-Str. 1, 53115 Bonn

Time	Session	Speaker
14:25–14:30	Welcome Speech	&
14:30–15:00	Proteins Under the Microscope: Extraction, Modification and Characterisation
15:00–15:10	Effects of processing on protein-bound amino acids
15:10–15:30	Innovative plant protein sources: Common bean and hempseed press cake as raw material for protein ingredient production	&
15:30–16:00	Break	—
16:00–16:15	From Questions to Discoveries: Highlights from My Food Science Research Career
16:15–16:30	Driving Excellence in Nutrition and Food Science: LAU’s NFS Department’s Mission and Work
16:30–16:40	Micro and Nanoplastics in table salt: occurence and exposure	Hanin El Hajj
16:40–16:50	Unmasking gluten contamination in gluten-free products	Roa Echtay
16:50–17:10	Wrap-up Discussion	Shadi & Ismail
17:10–17:30	Lab Tour

We have the symposium a bit late as one of the key guest’s expertise in Food Science will only arrive in the afternoon

Day 4 — Friday, 12 December 2025

Roundtable discussion in our lab + Excursion Day (Self-Organized)** 📍 Baunscheidtstraße 17, 53113 Bonn

For the Excurssion, here are some suggested visits: Bonn Christmas Market, Cologne Cathedral, Chocolate Museum Köln.
📍 Bonn / Cologne region

Day 5 — Saturday, 13 December 2025

Departure Day
📍 Youth Hostel Bonn

Invited Keynote at the 12th International Conference on Intelligent Computing and Information Systems (ICICIS) in Cairo, Egypt

Tue, 25 Nov 2025 00:00:00 +0000

I had the pleasure to give a keynote on Leveraging Collective Intelligence for Inclusive Global Healthcare at the 12th International Conference on Intelligent Computing and Information Systems (ICICIS) in Cairo, Egypt. I had the chance to meet and discuss further collaboration with the dean at the Ain Shams University Prof. Dr. Rasha Ismail, Prof. Dr. Hassan Abdalla from East London University, and Prof. Robert Sloan, from University of Illinois Chicago, and Prof. Huan Liu from Arizona Satet University.

Contributed to the GLOHRA’s fact-finding mission to Brussels

Mon, 17 Nov 2025 00:00:00 +0000

I recently had the privilege of joining the Steering Committee of the German Alliance for Global Health Research (GLOHRA) in Brussels for a fact-finding mission from 17–20 November 2025. Alongside colleagues from diverse research disciplines, we engaged in constructive dialogue with key European institutions and partners to better understand the EU’s policy and funding landscape for global health research. Over three days of meetings, we gained valuable insights into emerging priorities for research funding and the upcoming EU Framework Programme (FP10), identifying new opportunities for collaboration and coordinated action from the research community. This visit not only strengthened our understanding of how global health research can be supported at the EU level but also reinforced our collective commitment to advancing impactful, interdisciplinary research that addresses global health challenges.

MRIQT: Physics-Aware Diffusion Model for Image Quality Transfer in Neonatal Ultra-Low-Field MRI

Mon, 17 Nov 2025 00:00:00 +0000

MRIQT

Sun, 02 Nov 2025 00:00:00 +0000

About the method

From the abstract of :

Portable ultra-low-field MRI (uLF-MRI, 0.064 T) offers accessible neuroimaging for neonatal care but suffers from low signal-to-noise ratio and poor diagnostic quality compared to high-field (HF) MRI. We propose MRIQT, a 3D conditional diffusion framework for image quality transfer (IQT) from uLF to HF MRI. MRIQT combines realistic K-space degradation for physics-consistent uLF simulation, v-prediction with classifier-free guidance for stable image-to-image generation, and an SNR-weighted 3D perceptual loss for anatomical fidelity. The model denoises from a noised uLF input conditioned on the same scan, leveraging volumetric attention-UNet architecture for structure-preserving translation. Trained on a neonatal cohort with diverse pathologies, MRIQT surpasses recent GAN and CNN baselines in PSNR 15.3% with 1.78% over the state of the art, while physicians rated 85% of its outputs as good quality with clear pathology present. MRIQT enables high-fidelity, diffusion-based enhancement of portable ultra-low-field (uLF) MRI for deliable neonatal brain assessment.

Using the code

The repository README covers setup, test on a data sample, train on your own dataset, prepare your data in ., generate your own transfer function from your paired data. See for step-by-step instructions.

David participated with an oral presentation at the 10th BIGS Clinical & Population Science Student Retreat

Mon, 27 Oct 2025 00:00:00 +0000

On 27–28 Oct 2025 in Bonn, I attended the 10th BIGS Clinical & Population Science Student Retreat—a focused two-day program across the life sciences that highlighted post-PhD career pathways and the central role of human-in-the-loop AI in healthcare—featuring inspiring keynotes, the EnaCom “Research-to-Use-Case” workshop, and targeted networking.

In the oral presentation I showcased our work, “Multi-center Collaboration for Improved Lymph Node Metastasis Detection in PDAC.” Building on [1], commenting on the retrospective multicenter cohort of 652 PDAC patients (391 with lymph-node metastases; 59.97%), with the expanded two German sites—University Medicine Berlin (BER) and University Medical Center Göttingen (GÖT)—to include a more rigorously stress-test our two-stage deep-learning pipeline.

[1] David Dueñas Gaviria, Patrick Kupczyk, Belinda Lee, Peter Gibbs, Hyun Ko, Alexander Semaan, and Shadi Albarqouni. Deep learning for lymph node metastasis detection in pancreatic ductal adenocarcinoma. In Ruidan Su, Alejandro F. Frangi, and Yudong Zhang, editors, Proceedings of 2024 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2024), pages 66–76, Singapore, 2025. Springer Nature Singapore.

BILD — Summer 2025

Mon, 01 Sep 2025 00:00:00 +0000

Our lab at Universitätsklinikum Bonn, The University of Bonn, and Helmholtz AI in cooperation with with the Lebanese American University, University of Tunis El Manar, and Duhok Polytechnic University are delighted to announce the upcoming BILD Summer School in Tunis, Tunisia between 1 and 5 September 2025.

BILD 2025 is organized under the umbrella of the , uniting academic excellence and technological innovation across borders. Over five intensive days in Tunis, you will explore cutting-edge deep-learning techniques for medical imaging through expert lectures, hands-on labs, and collaborative case studies. Engage with peers and faculty from Germany, Lebanon, Iraq, and Tunisia as you develop practical skills in building and deploying AI models for real-world healthcare challenges. We look forward to an inspiring week of interdisciplinary exchange and the shared commitment to advancing affordable, life-saving AI in medicine.

With participation from diverse academic backgrounds, we foster knowledge transfer and collaborative learning among approximately 50 under-/ graduates, and faculty members from Germany and Arab countries.

Apply before 14th July 2024 and join us as we blend cutting-edge AI with diverse disciplines through lectures, labs, and a real-world hackathon. Travel tickets, accommodation, and catering will be covered for the whole 5 days of the summer school.

Further details about the summer school and the application:

Participated at the AI for Good Global Summit in Geneva, Switzerland

Tue, 08 Jul 2025 00:00:00 +0000

Just returned from my first AI for Good Global Summit in Geneva — an incredible experience!

I was especially inspired by the focus on AI in healthcare:

Reversing diabetes and Parkinson’s with AI-powered stem cell therapies
Real-time, personalized health risk screening using AI
Tackling global health inequities with open, responsible, and equitable AI solutions

This summit reminded me that AI is not just about innovation — it’s about impact. Excited to keep learning and contributing to this global movement

Panelist at Applied Case Studies of AI in Health for WHO EMRO Workshop, Egypt

Wed, 02 Jul 2025 00:00:00 +0000

Honored to join an inspiring panel at the WHO EMRO AI Workshop on Applied Case Studies of AI in Health, alongside brilliant colleagues working at the intersection of AI, public health, and policy. I shared insights from our work at the University of Bonn on building trustworthy and equitable AI models for medical imaging—especially in global health settings where data diversity and clinical applicability are key challenges. Grateful for the engaging discussion on how we can ensure that AI innovations serve not just high-resource settings, but also strengthen health systems worldwide

Invited Talk at the Andalusian Research Institute in Data Science and Computational Intelligence (DASCI), Spain

Wed, 18 Jun 2025 00:00:00 +0000

I had the pleasure to give a talk on Leveraging Collective Intelligence for Inclusive Global Healthcare at the ! Thanks to Prof. for the invitation and great hospitality! The Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI Institute), is a research institute promoted by the universities of Granada (UGR), Jaén (UJA) and Córdoba (UCO).

Contributed to the GLOHRA SC Meeting and GLOHRA Day in Berlin, Germany

Thu, 05 Jun 2025 00:00:00 +0000

At this year’s GLOHRA Steering Committee Meeting and GLOHRA Day, I helped put AI’s real-world value for global health firmly on the agenda. Co-leading the SC round-table with Prof. Charlotte Koehler, I steered debate away from hype toward actionable next steps. Later, at the Bundestag Parliamentary Evening, we participated in an interesting veent on Global Health in LMICs.

The next day, Jana Fehr and I ran an “Ask the Community” session to crowd-source how we should measure impactful AI. Participants mapped indicators for health outcomes, equity, and system sustainability, then agreed to co-develop a mixed-methods framework all projects can plug into—so evidence accumulates across diseases and settings. The experience clarified my role as a bridge between data science, health systems, and policy.

OP0343 The ClaudicAItio Model: Ultrasound Image Classification in Giant Cell Arteritis by Application of Artificial Intelligence

Sun, 01 Jun 2025 00:00:00 +0000

Summer School on Biomedical Imaging with Deep Learning (BILD)

Sun, 01 Jun 2025 00:00:00 +0000

Participated at the GLOHRA Training on Public Affairs in Berlin, Germany

Wed, 14 May 2025 00:00:00 +0000

What an amazing two days in Berlin! 🇩🇪 I joined the GLOHRA Steering Committee’s public affairs training, sharpening skills in stakeholder mapping, policy briefs, and advocacy pitches.Great to see GlobalHealth in the Koalitionsvertrag—now let’s 1) cut bureaucracy in funding, 2) use development budgets for research, and 3) build sustainable international partnerships

Thanks to Boris Barth, German Alliance for Global Health Research (GLOHRA), and all participants for their insights and enthusiasm!

Contributed to the 23rd BONFOR Symposium in Bonn, Germany

Wed, 07 May 2025 00:00:00 +0000

At the 23rd BONFOR Symposium on Wednesday, 7 May 2025, held in the DZNE Lecture Hall at University Hospital Bonn, Albarqouni Lab actively showcased its current research by delivering concise oral presentations during the afternoon’s Doctoral Promotion Fair. Our team highlighted a diverse portfolio of projects—ranging from advanced image‐analysis pipelines to AI-driven diagnostic tools—and outlined potential MD-thesis topics for interested students, fostering in-depth discussions about methodological approaches and future research directions. By engaging directly with medical students, the lab not only raised awareness of its work but also provided guidance on how to embark on cutting-edge thesis projects within our group, thereby strengthening the pipeline of young clinician-scientists

Invited Lecture at the Charité – Universitätsmedizin Berlin, Germany

Thu, 10 Apr 2025 00:00:00 +0000

As an Invited Lecturer for the Digital Health and Artificial Intelligence module at Charité – Universitätsmedizin Berlin’s Institute of International Health, I delivered a focused lecture on deep learning algorithms and their medical applications—highlighting federated learning—and had the privilege of engaging with a diverse, global cohort of brilliant medical professionals from Germany, other European countries and the U.S., Africa, the Eastern Mediterranean Region, Asia, and South America.

Invited Talk at the 3rd Symposium on International Pathways to Precision Medicine Symposium (P2M Symposium), Rouen, France

Thu, 03 Apr 2025 00:00:00 +0000

I had the honor of delivering an invited talk at the p2m Symposium, where I had the opportunity to share my work and connect with distinguished experts in the field. The event was an enriching platform that allowed me to engage directly with thought leaders, including Ellen GRANT from Harvard, Fouad CHEBIB from Mayo Clinic, and Ivana Dabaj from Rouen. I am particularly grateful to the event chairs, Soumaya and Abdellah from Rouen, whose exceptional leadership and support made this gathering truly memorable.

Invited Talk at the German University in Cairo and the American University in Cairo, Egypt

Tue, 04 Mar 2025 00:00:00 +0000

Just wrapped up two intense and inspiring days in Cairo at the The German University in Cairo and The American University in Cairo (AUC)! 🇪🇬

From invited talks and an engaging workshop to thesis defenses and valuable networking events, it was a fantastic opportunity to exchange ideas and connect with brilliant minds.

A big thank you to the hosts, Dr. Mohammed A.-Megeed Salem and Dr. Seif Eldawlatly and everyone who made this visit so enriching!

Looking forward to future hashtag#collaborations.

AI in Radiology – From Pixels to Decisions (joint teaching with GUC)

Wed, 01 Jan 2025 00:00:00 +0000

This joint teaching activity was developed within a BMFTR-funded international program to strengthen academic exchange and capacity building in artificial intelligence for medical imaging. The course combines lectures, hands-on exercises, and project-based learning, enabling students to work on clinically motivated imaging problems using AI methods.

Perspective

The initiative contributes to sustainable academic exchange between Germany and Egypt and provides a basis for continued joint teaching activities, student exchange, and integration of research-led topics into international curricula.

Selected Interdisciplinary Research with Methodological AI Contributions

In addition to my core research in artificial intelligence for medical imaging and clinical applications, I have contributed to a small number of exploratory interdisciplinary projects in materials science and food science. These activities focused on transferring methodological expertise in data-driven modeling and machine learning to neighboring domains and align with the University of Bonn’s Transdisciplinary Research Areas Sustainable Futures, Modelling, and Life and Health. Together, they demonstrate the transferability of AI methodologies and my engagement in transdisciplinary research initiatives without detracting from my primary research focus.

An efficient screening of dissolution modulators of Mg alloys: Combining high-throughput multi-well exposure with topographical quantification of volume loss and pitting corrosion

Wed, 01 Jan 2025 00:00:00 +0000

Artery-Fusion Deep Learning for Enhanced Ultrasound Diagnosis of Giant Cell Arteritis

Wed, 01 Jan 2025 00:00:00 +0000

Bias and generalizability of foundation models across datasets in breast mammography

Wed, 01 Jan 2025 00:00:00 +0000

Breast Mammography Dataset (Lebanon & international cohorts)

Wed, 01 Jan 2025 00:00:00 +0000

Partner: Dr. Charbel Mourad. Basis of our MICCAI 2025 study on bias and generalisability of foundation models across mammography datasets.

Conjunctival Melanocytic Intraepithelial Lesion (CMIL) Histopathology Dataset

Wed, 01 Jan 2025 00:00:00 +0000

Curated with Prof. Martina C. Herwig-Carl (Ophthalmic Pathology, UKB) together with University Hospitals Tübingen and Erlangen.

Exploring the limit of image resolution for human expert classification of vascular ultrasound images in giant cell arteritis and healthy subjects: the GCA-US-AI project

Wed, 01 Jan 2025 00:00:00 +0000

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Wed, 01 Jan 2025 00:00:00 +0000

Giant Cell Arteritis Ultrasound Dataset

Wed, 01 Jan 2025 00:00:00 +0000

Curated with the Center for Rare Rheumatological Diseases (Claus-Jürgen Bauer, Prof. Valentin Schäfer), University Hospital Bonn. One of the largest clinically validated datasets for ultrasound-based GCA diagnosis; being extended towards a multi-center German cohort.

Our lab led the ML-readiness: quality control, cleaning, standardised preprocessing, annotation workflow and training/validation splits.

Investigation of the image resolution limit for human interpretation of ultrasound images in giant cell arteritis and healthy subjects-the GCA-US-AI project

Wed, 01 Jan 2025 00:00:00 +0000

Leveraging Collective Intelligence for Inclusive Global Healthcare

Wed, 01 Jan 2025 00:00:00 +0000

Neonatal Ultra-Low-Field / High-Field MRI Dataset

Wed, 01 Jan 2025 00:00:00 +0000

Curated with the Department of Experimental Neonatology, University Hospital Bonn. Basis of MRIQT, a physics-aware 3D diffusion model for image-quality transfer; expert clinicians rated 85% of generated images as diagnostically useful.

Retinal Fundus Image Dataset

Wed, 01 Jan 2025 00:00:00 +0000

International collaboration with Dr. Said El Shamieh (Beirut Arab University, Lebanon). Harmonisation, label mapping and bias/generalisation evaluation protocols led by our lab.

Strategic Arab-German Network for Affordable and Democratized AI (SANAD)

Wed, 01 Jan 2025 00:00:00 +0000

In recent years, the transformative potential of Artificial Intelligence (AI) in revolutionizing global healthcare has become evident, offering promising advancements in diagnostic precision, personalized treatment, and healthcare delivery. However, integrating AI into low-resource environments, especially in low- and middle-income countries (LMICs), remains constrained by significant barriers such as limited computational resources, inadequate local data, and financial constraints. The Strategic Arab-German Network for Affordable and Democratized AI in Healthcare (SANAD- سند) seeks to address these challenges by fostering collaboration, empowering local expertise, and advancing Affordable AI and Collaborative Federated Learning solutions tailored to underserved regions. The word “SANAD” means support in Arabic, which is what this project is about.

SANAD aims to bridge the healthcare gap between resource-rich and resource-limited settings, particularly the Global North and Global South disparity. Many existing AI models are developed using centralized datasets from well-funded institutions, which often fail to generalize across diverse populations and contexts. SANAD proposes a strategic, inclusive approach to democratizing access to AI by leveraging interdisciplinary expertise and promoting knowledge-sharing between Arab and German stakeholders

Measures:

, Tunisia
, Germany

Stronger together: advancing equity in global health research partnerships

Wed, 01 Jan 2025 00:00:00 +0000

Potential Collaboration on organizing a Summer School on AI in Medicine, Tunisia

Tue, 26 Nov 2024 00:00:00 +0000

Last week, I had the privilege of meeting Prof. Dr. Moez CHAFRA, President of the University of Tunis El Manar, and his esteemed colleagues. We had a productive discussion exploring potential collaborations, including the exciting possibility of organizing a Summer School on AI in Medicine in Tunisia, among other initiatives.

Excited to see what the future holds—stay tuned for updates!

Participate at the World Health Summit in Berlin

Sun, 13 Oct 2024 00:00:00 +0000

At the World Health Summit, I had the opportunity to participate in key sessions moderated by GLOHRA, such as “Stronger Together: Improving Research Partnerships with LMICs,” where we heard from influential figures, including parliament members, and sessions like “Bridging Policy and Research: Translating the EU Global Health Strategy into Action” and “Building Success: On the Road to the ‘Nutrition for Growth’.” A few key takeaways from these discussions included the importance of: i) fostering communication that respects diverse cultures, ii) upholding the 3Es—engagement, equity, and ethics—in our partnerships, and iii) delivering impact and ensuring its sustainability in LMIC research collaborations. Moreover, the focus on nutrition for all, especially through affordable diet programs in LMICs, is critical. Capacity building in nutrition, potentially through AI and technology, also emerged as a vital strategy for driving progress.

Press release on our AGYA Summer School on Affordable AI -- written by Luther J. Kanso from LAU, Lebanon (19.08.2024)

Mon, 19 Aug 2024 00:00:00 +0000

Press release on our AGYA Summer School on Affordable AI at UKBonn (19.08.2024)

Mon, 19 Aug 2024 00:00:00 +0000

Organzie the Summer School on Affordbale AI (SAAI -- سعي)

Mon, 22 Jul 2024 00:00:00 +0000

We have just wrapped up an amazing week at the .

As the curtains close on an unforgettable week in Bonn, we reflect on the enriching experiences shared at the AGYA Summer School on Affordable AI. We were thrilled to host 60 guests, including 35 students from nations across the globe; 🇲🇷 Mauritania, 🇲🇦 Morocco, 🇹🇳 Tunisia, 🇩🇿 Algeria, 🇱🇾 Libya, 🇪🇬 Egypt, 🇵🇸 Palestine, 🇱🇧 Lebanon, 🇯🇴 Jordan, 🇸🇾 Syria, 🇮🇶 Iraq, 🇴🇲 Oman, 🇶🇦 Qatar, 🇰🇼 Kuwait, 🇸🇦Saudi, 🇦🇪 UAE, 🇾🇪 Yemen, 🇰🇲 Comoros, 🇩🇪 Germany, 🇷🇺 Russia, 🇮🇹 Italy, 🇺🇦 Ukraine, 🇫🇷 France, and 🇨🇴 Colombia alongside 15 faculty and AGYA members, and 6 dedicated tutors.

🚀 Our diverse participants engaged deeply with innovative AI concepts, learning from esteemed speakers, lecturers, and each other. The collaborative environment fostered not only education but also meaningful connections across cultural lines.

🌐 Social events at iconic locations like the Cologne Cathedral, Drachenburg Castle, and the Haus der Geschichte Museum provided fantastic backdrops for our participants to unwind and network, enhancing their cultural and academic experience.

Thanks to everyone who made this event a huge success—your enthusiasm and insights have truly shaped this summer school into a beacon of learning and international cooperation.

Special Thanks to the Arab-German Young Academy of Sciences and Humanities (AGYA) for the financial support. I would like to thank The University of Bonn, Universitätsklinikum Bonn, Helmholtz AI for the host and financial support as well.

SAAI — Summer 2024

Mon, 22 Jul 2024 00:00:00 +0000

Our lab at Universitätsklinikum Bonn, The University of Bonn, and Helmholtz AI in cooperation with the Arab-German Young Academy of Sciences and Humanities (AGYA) are delighted to announce the upcoming AGYA Summer School on Affordable Artificial Intelligence (SAAI -سعي) in Bonn, 22-26 July 2024.

The AGYA Summer School is designed to promote theoretical and experimental research in Affordable Artificial Intelligence (AI). Through a carefully curated interdisciplinary curriculum, participants will explore the fundamental principles, potential applications, and the latest advancements in this dynamic scientific field with a multi-faceted focus engaging the academic fields of health, education, and climate change.

With participation from diverse academic backgrounds, we foster knowledge transfer and collaborative learning among approximately 30 under-/ graduates, and 12 faculty members from Germany and Arab countries.

Apply before 29th May 2024 and join us as we blend cutting-edge AI with diverse disciplines through lectures, labs, and a real-world hackathon. Travel tickets, accommodation, and catering will be covered for the whole 5 days of the summer school.

Further details about the summer school and the application:

Invited Keynote Lecture on AI in Ophthalmology and Pathology

Sat, 15 Jun 2024 00:00:00 +0000

I was invited to give a Keynote Lecture on AI in Ophthalmology and Pathology to present a few works on Histopathology; Mitotoc figure detection in Breast Cancer, Domain Adaptation from H&E Imaging to IHC imaging among others. Thanks Prof. Martina C. Herwig-Carl for the invitation and warm hospitality!

Summer School on Affordable AI (SAAI)

Wed, 01 May 2024 00:00:00 +0000

Research Visit at the University of Sharjah, UAE

Sun, 14 Apr 2024 00:00:00 +0000

I have just completed the research visit at the University of Sharjah, NYU Abu Dhabi, and other esteemed universities. This initiative aims to strengthen connections and foster collaboration between German and Emirati academic institutes in the field of health and medical sciences. I will soon return to the Emirates to organize a one-day workshop on AI and healthcare at a prestigious academic institute. Stay tuned for more details!

Participate at the AGYA Winter School on Quantum Materials

Mon, 12 Feb 2024 00:00:00 +0000

It was a pleasure participating at the AGYA Winter School on ‘Quantum Materials for Energy and Environmental Technologies’ at Sharjah, UAE. The school seamlessly integrates networking opportunities for both Arab and German students, alongside expert presentations focusing on new materials for sustainable energy technologies.

Invited to a Panel Discussion on AI in clinical routine at LMU, Munich, Germany

Fri, 19 Jan 2024 00:00:00 +0000

I was excited to join a distinguished panel, including Prof. Dr. Wolfgang Böcker, Prof. Dr. Clemens Cyran, and Katharina Danhauser, moderated by Claus Belka, to explore AI’s readiness for clinical routine. Our discussion spotlighted crucial aspects: the current state of AI in clinics and strategic placement within workflows. Emphasis was placed on robust data governance and addressing development challenges. The dialogue highlighted the importance of collaboration with stakeholders, involving regulatory bodies, and prioritizing education and training. Grateful for the opportunity to contribute to this pivotal conversation shaping the future of AI in healthcare. Thanks for the invitation, Guillaume Landry!

Affordable Deep Learning for Diagnosing Inherited and Common Retinal Diseases via Color Fundus Photography

Mon, 01 Jan 2024 00:00:00 +0000

Bias-aware foundation models for equitable breast cancer screening

Mon, 01 Jan 2024 00:00:00 +0000

This project addresses fairness and generalization challenges of foundation models for breast mammography, where AI systems trained predominantly on data from high-income countries often underperform in underrepresented populations. Together with international clinical partners, we assembled and analyzed a heterogeneous collection of mammography datasets from Europe, North America, the Middle East, and Asia, including a biopsy-confirmed in-house cohort of approximately 1,500 patients from Lebanon. Methodologically, we conducted a systematic evaluation of foundation models under cross-dataset shift and demonstrated that naïve dataset aggregation improves average performance but does not eliminate subgroup-specific biases. Fairness-aware optimization strategies substantially reduced performance disparities across age groups, breast density categories, and geographic regions while maintaining competitive diagnostic accuracy.

Perspective

The collaboration constitutes an ongoing international research effort at the intersection of medical imaging, fairness-aware AI, and global health. Building on current results, the project is being expanded to additional clinical partners in Palestine and Egypt, supported by the Global One Health Research Academy (GLOHRA), and is planned to scale further through the submitted EU Horizon Europe project SHE-Scan, contingent on funding.

INTERACT-CMIL: Interpretable multi-task grading of conjunctival melanocytic lesions

Mon, 01 Jan 2024 00:00:00 +0000

Collaborators: University Hospital Tübingen, University Hospital Erlangen

This project addresses the grading of conjunctival melanocytic intraepithelial lesions (CMIL), rare precursors of conjunctival melanoma characterized by complex, interdependent histopathological criteria and substantial inter-observer variability. Together with clinical partners, we curated a multi-center, expert-annotated histopathology dataset comprising 486 conjunctival biopsy image patches labeled along five clinically defined grading axes. Methodologically, we developed INTERACT-CMIL, a multi-task deep learning framework modeling inter-criterion dependencies via selective multi-head supervision and an inter-task consistency loss.

The proposed approach outperformed CNN and pathology foundation-model baselines (macro-F1 up to 85%), producing coherent and interpretable predictions aligned with expert grading standards. Methodological development and evaluation were coordinated within my group in close collaboration with the clinical partners.

Perspective

The project established a reproducible computational benchmark for CMIL grading and contributed to the standardization of digital ocular pathology. A related follow-up project on AI-assisted eyelid tumor analysis received a University of Bonn Prototype Grant, supporting translational development and clinical feasibility studies.

MRIQT: Image-quality transfer from ultra-low-field to high-field neonatal MRI

Mon, 01 Jan 2024 00:00:00 +0000

This project addresses the limited diagnostic usability of portable ultra-low-field MRI (0.064 T) in neonatal neuroimaging, where reduced signal-to-noise ratio constrains clinical interpretation despite bedside accessibility. Together with the clinical partners, we curated a unique in-house cohort comprising 50 paired ultra-low-field and high-field 3D neonatal brain MRI scans, complemented by 230 additional unpaired scans across nine pathologies. Building on this paired–unpaired data structure, we developed MRIQT, a 3D conditional diffusion framework for structure-preserving image quality transfer from ultra-low-field to high-field MRI. The method integrates physics-consistent k-space degradation, classifier-free guided diffusion, and an SNR-weighted volumetric perceptual loss.

MRIQT outperformed CNN- and GAN-based baselines, achieving up to 15.3% improvement in PSNR, while expert clinicians rated 85% of generated images as diagnostically useful with preserved pathology.

Perspective

The project provides a methodological foundation for automated MRI quality enhancement and modality-agnostic quality control. Building on the current results, the work is prepared for translational follow-up and prospective validation, with a planned submission to the Go-Bio Initial funding program.

Robust Federated Learning Methodology for Automatic Chest X-Ray Diagnosis

Mon, 01 Jan 2024 00:00:00 +0000

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions--An EndoVis' 24 Challenge

Mon, 01 Jan 2024 00:00:00 +0000

EEDA — Winter 2023/24

Thu, 14 Dec 2023 00:00:00 +0000

Welcome/Willkommen (de)/اهلا وسهلا (ar) to the AI Autumn School, one of the measures of our project on affordablE collaborativE learning for global Digital heAlth (EEDA – ايدا), a platform where academia meets innovation, and global collaboration sparks intellectual excellence. Through the partnership of Albarqouni Lab at the University Hospital Bonn, University of Bonn and Helmholtz Munich in Germany and the Faculty of Health Sciences at Beirut Arab University in Lebanon, we offer an enriching educational experience that marries cutting-edge AI advancements with multi-discplines through lectures, hands-on labs, and real-world hackathon led by faculty and skilled tutors. Further details about the autumn school and the application:

Organize the Autumn School on AI (EEDA - ايدا)

Thu, 14 Dec 2023 00:00:00 +0000

Thrilled to share the success of our EEDA Autumn School on AI – a global collaboration between Albarqouni Lab (Germany), Universitätsklinikum Bonn, The University of Bonn, and the Faculty of Health Sciences at Beirut Arab University (Lebanon). Over the weekend, a distinguished Lebanese delegation from the Beirut Arab University ( BAU), American University of Beirut ( AUB), and Lebanese American University ( LAU) joined us for an immersive program blending cutting-edge AI with diverse disciplines.

Bridging Gaps, Fostering Excellence: This initiative, supported by the DAAD German Academic Exchange Service Ta’ziz Short Measures Grant, goes beyond an event – a testament to global collaboration. Empowering minds and breaking barriers, we achieved an 80% female participation rate and a competitive 26% acceptance rate, highlighting our commitment to academic excellence.

International Collaboration in Action: Thanks to DAAD, the funding from the Auswärtiges Amt (Federal Foreign Office) Germany, and the endorsement of Arab-German Young Academy of Sciences and Humanities (AGYA), the EEDA Autumn School on AI has sparked meaningful collaborations, from curriculum integration to potential joint Master’s Programs. We are creating a ripple effect of international cooperation and academic brilliance.

Beyond Academia: Our journey surpassed the academic realm, fostering a profound cultural exchange. It’s not just about AI; it’s about building a global community that values diversity, mutual respect, and inclusive collaboration. The delegation enjoyed the social events from visiting the Christmas Market, Cathedral Kolner Döm, to the Haus der Geschichte.

Despite the challenging times, let’s continue breaking barriers, fostering innovation, and creating a better, more connected world. The success of this weekend is just the beginning – together, we can rise!

Photo credits to Paul Nuhn, Universitätsklinikum Bonn

Affordable AI and Collaborative Federated Learning for Global Healthcare (EEDA)

Fri, 01 Sep 2023 00:00:00 +0000

Fact Finding Mission to the United Arab Emirates

Sun, 19 Feb 2023 00:00:00 +0000

Just wrapped up an incredible 72-hour agya fact-finding mission with the brilliant Vice-Presidents and Provosts of top universities in Dubai and Abu Dhabi, UAE. The main goal of our fact-finding mission is to promote the call for membership of the Arab-German Young Academy of Sciences and Humanities (AGYA)!

Dr. Nada El Darra and I had the pleasure to meet Vice-President Sultan Al Haji, the Acting Provost Prof. Tim Baldwin at MBZUAI (Mohamed bin Zayed University of Artificial Intelligence), and the Acting Provost and Chief Academic Officer Prof. Michael Allen at Zayed University besides our brilliant colleagues Dr. Rania Dghaim, Dr. Fatme Al Anouti, and Dr. Omar Alfandi.

Thanks to our alumna, Dr. Henda Mahmoudi, and her colleagues Nour El Jundi and Evans Kioko, we very much enjoyed the wonderful field trip at International Center for Biosaline Agriculture (ICBA) where we discussed potential interdisciplinary research collaboration on AI and Food Safety!

Invited Talk at 3MT Competition at Beirut Arab University

Fri, 10 Feb 2023 00:00:00 +0000

Today I was privileged to give a keynote speech at the Beirut Arab University in Lebanon 🇱🇧. My talk was about the impact of AI in the field of Medicine and how AI is changing the way healthcare professionals diagnose, treat, and care for hashtag#patients. My presentation included a couple of examples from our hashtag#research on microscopic imaging, but I also looked at a couple of hashtag#startups that are taking this to the next level by transforming healthcare through their hashtag#innovative products. Having a fruitful discussion with the audience was a real pleasure for me! Thanks so much, Dr. Nada El Darra and Dr. Said El Shamieh, for the warm welcome and the invitation 🙏

Deep learning to estimate aging from chest imaging

Sun, 01 Jan 2023 00:00:00 +0000

Chronologic age is an important risk factor for many chronic diseases. However, as we all age at different rates chronological age is an imperfect measure of aging and more accurate estimates of one’s true biological age are desirable. The proposed study will develop and test artificial intelligence-based measures of aging from chest imaging data and will explore whether these measures can improve chronologic age-based clinical guidelines for prevention of cardiovascular disease and cancer. Over recent decades life expectancy has continuously increased, however, the years of life without significant morbidity have remained stable over the last 5 decades resulting in significant socioeconomic burden to society and the healthcare system. Therefore, delaying the onset of morbidity is a promising way to improve healthy aging. In particular, prevention and early detection of diseases of high socioeconomic importance such as cancer and cardiovascular disease are a priority to increase health and longevity. Biological age is a concept to estimate the differences in rates of aging not captured by chronologic age. Several measures of biological age have been developed in recent years including blood, functional and physiological approaches. However, there relevance in clinical routine is limited. Medical imaging may be an effective way to estimate aging by measuring age-related changes based on anatomical alterations visible in the image such as degenerative changes of the spine, dilation of the heart and vasculature and changes in lung parenchyma. Unlike molecular measures, image-based reflections of aging can be calculated opportunistically using data acquired during routine clinical care. Furthermore, these measures can estimate aging of specific organ systems, which may improve disease-specific preventive measures over general estimates of biological age. The image-based estimates of aging using deep learning proposed in this study will address this unmet need and may help to personalize clinical decision-making for chronic disease screening and prevention. My preliminary data show that deep learning can estimate a chest x-ray age from a chest radiograph image and that this chest x-ray age predicts longevity better than chronologic age in large multicenter clinical trial data. The proposed project will build on these promising initial findings with the potential to broaden the application to other imaging modalities and to leverage multi-omics data to infer causes of aging.

Collaborators:

PD. Dr. , UniKlinikum Freiburg

Outcomes

This exploratory project examined the feasibility of imaging-based phenotyping and biological aging estimation from chest CT and MRI. The work focused on conceptual development and clinical hypothesis generation rather than large-scale validation and resulted in a clinical abstract presented at the European Congress of Radiology.

Perspective

The project provided methodological insights into opportunistic imaging-based risk modeling and informed the design of subsequent projects with clearer data availability, validation strategies, and funding perspectives.

Digital staining of white blood cells with confidence estimation

Sun, 01 Jan 2023 00:00:00 +0000

Evolving the era of 5D ultrasound? A systematic literature review on the applications for artificial intelligence ultrasound imaging in obstetrics and gynecology

Sun, 01 Jan 2023 00:00:00 +0000

Guest Editorial Special Issue on Federated Learning for Medical Imaging: Enabling Collaborative Development of Robust AI Models

Sun, 01 Jan 2023 00:00:00 +0000

Lvm-med: Learning large-scale self-supervised vision models for medical imaging via second-order graph matching

Sun, 01 Jan 2023 00:00:00 +0000

Fact Finding Mission to Amman, Jordan

Wed, 21 Dec 2022 00:00:00 +0000

Just arrived from Amman, Jordan! It was just an amazing trip to Amman where I met our clinical partner King Hussein Cancer Foundation and Center for one of our projects supported by the Arab-German Young Academy of Sciences and Humanities (AGYA)! I was also happy to accept the invitation of my former PhD student, Tariq Bdair, and the head of his department, Dr. Serin Atiani, to deliver a talk at Princess Sumaya University for Technology! I was impressed by the level of their students, indeed! I would like also to thank Dr. Omar Al-Kadi from The University of Jordan for the invited talk and the great hospitality!

Co-Organizer of the Introduction of Bioinformatics to Arab Education System

Mon, 21 Nov 2022 00:00:00 +0000

I had the pleasure last week of co-organizing a workshop on Bioinformatics in Tunisia. The workshop was part of the working group activities supported by the Arab-German Young Academy of Sciences and Humanities! We had a great lineup of speakers from Germany/Switzerland and Arab countries! I had the pleasure to know a couple of Arab scholars from Egypt, Lebanon, Algeria, Tunisia, and Sudan.

Invited Talk at AI 4 Imaging

Wed, 29 Jun 2022 00:00:00 +0000

Organizing the 2nd version of the MICCAI Workshop on aFfordable AI and healthcare

Thu, 23 Jun 2022 00:00:00 +0000

Affordable deep learning for inherited and common retinal diseases

Wed, 01 Jun 2022 00:00:00 +0000

This international collaboration addresses the diagnosis of inherited retinal diseases (IRDs) and common retinal disorders using affordable color fundus photography, motivated by limited access to genetic testing and advanced imaging in low-resource settings. Together with partners in Germany and Lebanon, we curated and analyzed heterogeneous multi-center retinal imaging datasets combining in-house cohorts and public data, spanning diverse demographics and disease spectra. Methodologically, we developed lightweight deep learning models for multi-class retinal disease classification covering 21 retinal conditions, including 13 IRDs. The approach achieved strong diagnostic performance for prevalent IRDs (e.g., F1 score of 0.86 for retinitis pigmentosa) while remaining computationally efficient and interpretable.

Perspective

The collaboration established a sustainable international research link between ophthalmology groups in Germany and Lebanon and contributed to interdisciplinary student training. Ongoing work explores privacy-preserving and federated learning strategies to improve robustness and equitable deployment across populations further.

Co-Organizing the 10. DFG-#Nachwuchsakademie

Sun, 23 Jan 2022 00:00:00 +0000

Development of a Deep Learning Toolkit for MRI-Guided Online Adaptive Radiotherapy

Sat, 01 Jan 2022 00:00:00 +0000

In Germany close to 500,000 new patients are diagnosed with cancer every year. More than half of the patients undergo radiotherapy as part of their treatment. In-room imaging, in combination with modern external beam radiotherapy techniques, enables tight adjustment of the delivered dose to the target. However, the full potential of these techniques is currently not exploited. One of the reasons for this is the presence of anatomical changes. For the vast majority of patients in today’s radiotherapy, anatomical alterations are considered by introducing safety margins around the tumor volume during treatment planning, which increases the irradiated volume, the dose burden to organs at risk (OARs), and eventually limits the applicable dose.

An improved treatment can be realized by online adaptive radiation therapy (ART): instead of applying the same irradiation plan throughout the entire course of treatment, the treatment is optimized at each irradiation session on basis of the daily anatomy in treatment position, as inferred from in-room imaging. Great efforts have been made to realize the integration of magnetic resonance imaging (MRI) as in-room imaging modality, but only during the last few years integrated MR-linacs became clinically available at few academic institutions. The superior soft-tissue contrast allows for accurate visualization of targets and OARs, which has allowed basic online ART workflows to be implemented. The time overhead associated with the generation of a synthetic-CT from MRI and a re-delineation of the patient for treatment plan optimization greatly limits applicability to all patients.

The goal of this project is to tackle the main bottlenecks of online plan adaptation: synthetic CT generation and organ delineation. By combining our respective expertise in radiotherapy and artificial intelligence, we aim at proposing deep-learning-based solutions to build a toolkit to tackle both of these challenges. In the case of synthetic CT generation, we will use generative adversarial networks with hybrid paired-unpaired training paradigms, additionally exploiting novel embedding loss functions to ensure geometric fidelity. Alternatively, disentangled representations will be investigated. For segmentation we aim at exploiting the information from previous fractions by performing spatio-temporal segmentation using longitudinal spatial memory networks. Finally, we aim at evaluating the benefits from these approaches by performing an evaluation of their clinical impact in terms of plan optimization, including physician grading of delineations and robustness assessment. The time benefits, including potential corrections to segmentations and synthetic CT, will also be quantified. Further details can be found .

Collaborators:

Prof. , Department of Radiation Oncology, LMU University Hospital
Dr. , Department of Radiation Oncology, LMU University Hospital
Prof. , CAMP, CIT School, TU Munich

Outcomes

MRI-guided online adaptive radiotherapy requires fast and reliable image segmentation and synthesis to support treatment adaptation under strict clinical time constraints. In this DFG-funded project, we addressed key methodological challenges related to patient-specific segmentation and uncertainty-aware modeling under inter-observer and inter-institutional variability. Together with the partners, we curated a longitudinal multi-center MRI cohort comprising planning and fraction scans across multiple institutions, enabling systematic evaluation under realistic clinical heterogeneity. Methodologically, the project resulted in two complementary contributions: CloverNet, a patient-specific 3D segmentation architecture leveraging prior planning annotations, and ProSona, a prompt-guided personalization framework modeling inter-expert variability via a continuous latent space. The proposed methods achieved a significant improvement over standard baselines and received a Best Paper Award at a MICCAI workshop.

Perspective

The project established a strong national research network at the interface of medical imaging, radiation oncology, and AI, and contributed to doctoral and master’s training. Ongoing work extends these approaches toward scalable foundation models for personalized and uncertainty-aware radiotherapy workflows, with a focus on clinical integration and multi-center validation.

Distributed, collaborative, and federated learning, and affordable AI and healthcare for resource diverse global health

Sat, 01 Jan 2022 00:00:00 +0000

GCA-US-AI: Deep learning–based ultrasound diagnosis of Giant Cell Arteritis

Sat, 01 Jan 2022 00:00:00 +0000

In close interdisciplinary cooperation with clinical rheumatology, this project addresses the highly operator-dependent and time-critical ultrasound diagnosis of Giant Cell Arteritis (GCA), a vasculitis associated with irreversible vision loss. Together with our partners, we curated and annotated an in-house dataset of 3,800 ultrasound examinations from 344 patients, constituting one of the largest clinically validated datasets for ultrasound-based GCA diagnosis. Methodologically, we developed artery-aware deep learning models that integrate anatomical context into ultrasound image analysis, achieving robust performance improvements over artery-agnostic baselines (AUROC up to ~0.9 for larger arteries). Uncertainty estimation and visual explainability were incorporated to support reliable clinical interpretation.

Perspective

The collaboration is ongoing and continues to serve as a basis for joint publications, supervised theses, and continued dataset extension (in-house already ~15,000 images) toward a multi-center cohort. Current work explores vision–language models integrating imaging and clinical data and forms the basis of a planned joint DFG proposal coordinated together with the clinical partners.

Large vision models for robust and trustworthy medical image understanding

Sat, 01 Jan 2022 00:00:00 +0000

This collaboration investigates the applicability of large vision models to medical imaging, addressing robustness, generalization, and trustworthiness under domain shift and limited supervision. The work examined the behavior of foundation-scale models transferred from natural-image pretraining to medical imaging tasks. Methodological contributions include empirical analyses of cross-domain generalization, sensitivity to spurious correlations, and limitations of naïve scaling, alongside proposed evaluation protocols and architectural adaptations to improve robustness and reliability in medical contexts.

Perspective

The collaboration supported doctoral mentorship and strengthened national expertise at the intersection of foundation models and medical imaging. The insights gained inform ongoing work on principled evaluation and responsible deployment of large vision models in medical applications.

What can we learn about a generated image corrupting its latent representation?

Sat, 01 Jan 2022 00:00:00 +0000

Organizing the 1st MICCAI Workshop on aFfordable AI and healthcare

Wed, 23 Jun 2021 00:00:00 +0000

BigPicture Project

Mon, 01 Feb 2021 00:00:00 +0000

A new consortium of the EU Innovative Medicines Initiative (IMI) will establish the biggest database of pathology images to accelerate the development of artificial intelligence in medicine.

To take AI development in pathology to the next level, a European consortium combining leading European research centres, hospitals as well as major pharmaceutical industries, is going to develop a repository for the sharing of pathology data. The 6-year project called , will herald a new era in pathology.

Background

Pathology is the cornerstone of the workup of many diseases such as cancer, autoimmune diseases, of the follow up after transplantation and is also critical for the evaluation of the safety of drugs. It’s based on the examination of tissue samples (slides) under the microscope. However, despite its pivotal role, it still relies heavily on the qualitative interpretation by a qualified pathologist.

While the microscope symbolizes the profession, the digitalisation of slides in recent years ignited a revolution: not only images can now be shared and accessed from distant locations, they can also be processed by computers. This opens the door for artificial intelligence (AI) applications to assist the pathologist and help study diseases, find better treatments and contribute to the 3Rs (replace, reduce, and refine animal use in research). However, the development of robust AI applications requires large amounts of data, which in the case of pathology means a huge collection of digital slides and the medical data necessary for their interpretation. Sharing these has so far remained challenging due to the data storage capacity required to host a sufficiently large collection and to concerns regarding the confidential character of the medical information.

To allow the fast development of AI in pathology, the project aims to create the first European, ethical and GDPR-compliant (General Data Protection Regulation), quality-controlled platform, in which both large-scale data and AI algorithms will coexist. The platform will be developed in a sustainable and inclusive way by connecting communities of pathologists, researchers, AI developers, patients, and industry parties.

TU Munich

In this project, , and from , together with will be leading and contributing to the development of Federated Deep Learning algorithms leveraging massive amounts of data, distributed in multiple sources, in a privacy-preserved fashion. This will enable deep learning models to be trained using sensitive data that cannot be made publicly available due to GPDR or sensitivity, e.g. rare diseases. Please visit the website of for further details.

Intended results

The project is divided into four main aspects that concern the large-scale collection of data. First, an infrastructure (hardware and software) must be created to store, share and process millions of images that can be gigabytes each. Second, legal and ethical constraints must be put in place to ensure adequate usage of data while fully respecting patient’s privacy and data confidentiality. Then, an initial set of 3 million digital slides from humans and laboratory animals will be collected and stored into the repository to provide data for the development of pathology AI tools. Finally, functionalities that aid the use of the database as well as the processing of images for diagnostic and research purposes will be developed.

Domain Adaptation and Representation Transfer and Distributed and Collaborative Learning: Third MICCAI Workshop, DART 2021, and First MICCAI Workshop, FAIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021: Proceedings

Fri, 01 Jan 2021 00:00:00 +0000

Domain adaptation and representation transfer, and affordable healthcare and AI for resource diverse global health

Fri, 01 Jan 2021 00:00:00 +0000

Fourier Transform of Percoll Gradients Boosts CNN Classification of Hereditary Hemolytic Anemias

Fri, 01 Jan 2021 00:00:00 +0000

FUTURE-AI: International guidelines for trustworthy medical AI

Fri, 01 Jan 2021 00:00:00 +0000

This collaboration contributes to the development of international guidelines, evaluation frameworks, and best practices for trustworthy artificial intelligence in medical imaging and clinical decision support. Within the FUTURE-AI consortium, I contributed to survey- and consensus-driven publications synthesizing methodological, clinical, and regulatory perspectives on reliability, safety, fairness, and clinical validation of AI systems. The resulting publications define actionable evaluation criteria, identify common failure modes, and propose harmonized reporting and validation standards that support responsible clinical translation.

Perspective

Engagement in the FUTURE-AI consortium supports sustained international community-building and contributes to the maturation of evaluation standards for medical AI. Ongoing activities focus on refining guidelines and aligning academic research with emerging regulatory and translational requirements.

Semi-Supervised Few-Shot Learning with Prototypical Random Walks

Fri, 01 Jan 2021 00:00:00 +0000

Sickle Cell Disease Severity Prediction from Percoll Gradient Images using Graph Convolutional Networks

Fri, 01 Jan 2021 00:00:00 +0000

Multi-task multi-domain learning for digital staining and classification of leukocytes

Mon, 21 Dec 2020 00:00:00 +0000

The Future of Digital Health with Federated Learning

Mon, 14 Sep 2020 00:00:00 +0000

Fairness by Learning Orthogonal Disentangled Representations

Sat, 01 Aug 2020 00:00:00 +0000

Deep Federated Learning in Healthcare

Wed, 01 Apr 2020 00:00:00 +0000

Deep Learning (DL) has emerged as a leading technology for accomplishing many challenging tasks showing outstanding performance in a broad range of computer vision and medical applications. However, this success comes at the cost of collecting and processing a massive amount of data, which often are not accessible, in Healthcare, due to privacy issues. Federated Learning (FL) has been recently introduced to allow training DL models without sharing the data. Instead, DL models at local hubs, i.e. hospitals, share only the trained parameters with a centralized DL model, which is, in return, responsible for updating the local DL models as well.

Our golas in this project is to develop novel models and algorithms for a ground-breaking new generation of deep FL, which can distill the knowledge from local hubs, i.e. hospitals, and edges, i.e. wearable devices, to provide personalized healthcare services.

The principal challenges, to overcome, concern the nature of medical data, namely data heterogeneity; severe class-imbalance, few amounts of annotated data, inter-/intra-scanners variability (domain shift), inter-/intra-observer variability (noisy annotations), system heterogeneity, and privacy issues (see the example below).

Collaboration:

Organizing Committee Member at MICCAI DART 2020

Wed, 01 Apr 2020 00:00:00 +0000

Seamless Virtual Whole Slide Image Synthesis and Validation Using Perceptual Embedding Consistency

Wed, 19 Feb 2020 00:00:00 +0000

Keynote Speaker: AI in Healthcare

Wed, 08 Jan 2020 00:00:00 +0000

A learning without forgetting approach to incorporate artifact knowledge in polyp localization tasks

Wed, 01 Jan 2020 00:00:00 +0000

An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy

Wed, 01 Jan 2020 00:00:00 +0000

Digital staining with confidence estimation for hematological imaging

Wed, 01 Jan 2020 00:00:00 +0000

Collaborators: PD Dr.\ Slobodan Ilic (Siemens AG)

This project investigated the reliability of generative image translation for digital staining of hematological microscopy images, aiming to reduce variability and processing time in laboratory workflows while preserving diagnostically relevant structures. Using paired unstained and chemically stained microscopy images from 24 patients, we developed a style–structure disentangled generative framework with an associated confidence estimation that quantifies output reliability via latent perturbations. The confidence score correlated strongly with perceptual image quality and enabled the identification of unreliable synthetic samples.

Our role

Contributing collaborator with responsibility for methodological development and uncertainty estimation; doctoral supervision and mentorship conducted in close collaboration with academic and industrial partners.

Perspective

The project contributed to the successful doctoral training of an external PhD candidate and established methodological foundations for later work on uncertainty estimation and trust in medical image generation. It exemplifies sustained national collaboration between academia and industry.

Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning

Wed, 01 Jan 2020 00:00:00 +0000

Modelling Uncertainty in Deep Learning for Medical Applications

Wed, 01 Jan 2020 00:00:00 +0000

Deep Learning has emerged as a leading technology for accomplishing many challenging tasks showing outstanding performance in a broad range of applications in computer vision and medical applications. Despite its success and merit in recent state-of-the-art methods, DL tools still lack in robustness hindering its adoption in medical applications. Modeling uncertainty, through Bayesian Inference and Monte-Carlo dropout, has been successfully introduced to computer vision for better understanding the underlying deep learning models. In this proposal, we investigate modeling the uncertainty for medical applications given the well-known challenges in medical image analysis, namely severe class-imbalance, few amounts of labeled data, domain shift, and noisy annotations.

Collaboration:

Prof. , , .

Prof. , ,

Organizing Committee Member at MICCAI DART 2019

Sun, 13 Oct 2019 00:00:00 +0000

Keynote Speaker: Towards Deep Federated Learning in Healthcare

Thu, 19 Sep 2019 00:00:00 +0000

Deep autoencoding models for unsupervised anomaly segmentation in brain MR images

Tue, 01 Jan 2019 00:00:00 +0000

Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data: First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019, Shenzhen, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings

Tue, 01 Jan 2019 00:00:00 +0000

Focal loss for artefact detection in medical endoscopy

Tue, 01 Jan 2019 00:00:00 +0000

Generalizing multistain immunohistochemistry tissue segmentation using one-shot color deconvolution deep neural networks

Tue, 01 Jan 2019 00:00:00 +0000

Learning interpretable disentangled representations using adversarial vaes

Tue, 01 Jan 2019 00:00:00 +0000

Learning Interpretable Features via Adversarially Robust Optimization

Tue, 01 Jan 2019 00:00:00 +0000

Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer

Tue, 01 Jan 2019 00:00:00 +0000

Staingan: Stain style transfer for digital histological images

Tue, 01 Jan 2019 00:00:00 +0000

Towards an Interactive and Interpretable CAD System to Support Proximal Femur Fracture Classification

Tue, 01 Jan 2019 00:00:00 +0000

Virtualization of tissue staining in digital pathology using an unsupervised deep learning approach

Tue, 01 Jan 2019 00:00:00 +0000

Fusing unsupervised and supervised deep learning for white matter lesion segmentation

Thu, 13 Dec 2018 00:00:00 +0000

Invited Talk: Can Deep Learning Models be Trained with Annotations Collected via Crowdsourcing?

Sun, 09 Sep 2018 00:00:00 +0000

Capsule networks against medical imaging data challenges

Mon, 01 Jan 2018 00:00:00 +0000

Domain and geometry agnostic CNNs for left atrium segmentation in 3D ultrasound

Mon, 01 Jan 2018 00:00:00 +0000

Fine-Tuning Deep Learning by Crowd Participation

Mon, 01 Jan 2018 00:00:00 +0000

Generating highly realistic images of skin lesions with GANs

Mon, 01 Jan 2018 00:00:00 +0000

Scene coordinate and correspondence learning for image-based localization

Mon, 01 Jan 2018 00:00:00 +0000

When regression meets manifold learning for object recognition and pose estimation

Mon, 01 Jan 2018 00:00:00 +0000

Interview and Press Release on our IEE TMI Crowdsourcing Paper at the Biomedical Computation Review by Alexander Gelfand (30.08.2017)

Wed, 30 Aug 2017 00:00:00 +0000

Machine Learning for Biomedical Applications: From Crowdsourcing to Deep Learning

Sun, 01 Jan 2017 00:00:00 +0000

Semi-supervised deep learning for fully convolutional networks

Sun, 01 Jan 2017 00:00:00 +0000

X-Ray PoseNet: 6 DoF pose estimation for mobile X-Ray devices

Sun, 01 Jan 2017 00:00:00 +0000

Interview and Press Release on our IEE TMI Crowdsourcing Paper at TU Munich (04.08.2016)

Thu, 04 Aug 2016 00:00:00 +0000

Polyp and Artifact Detector

Wed, 20 Apr 2016 00:00:00 +0000

Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images

Fri, 01 Jan 2016 00:00:00 +0000

Playsourcing: a novel concept for knowledge creation in biomedical research

Fri, 01 Jan 2016 00:00:00 +0000

Steps towards Establishing Telemedicine Center in Palestine

Thu, 17 Sep 2009 00:00:00 +0000

Telemedicine in Palestine

Thu, 01 Jan 2009 00:00:00 +0000

Telemedicine in Palestine from Shadi Nabil Albarqouni

Collaboration:

Autumn School on AI (EEDA)

Mon, 01 Jan 0001 00:00:00 +0000

Summer School on Affordable AI (SAAI)

Mon, 01 Jan 0001 00:00:00 +0000

Summer School on Biomedical Imaging with Deep Learning (BILD)

Mon, 01 Jan 0001 00:00:00 +0000